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Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

Nature Neuroscience volume 22, pages 343–352 (2019)Cite this article

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Abstract

Major depression is a debilitating psychiatric illness that is typically associated with low mood and anhedonia. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximize sample size, we meta-analyzed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 genesets associated with depression, including both genes and gene pathways associated with synaptic structure and neurotransmission. An enrichment analysis provided further evidence of the importance of prefrontal brain regions. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant after multiple testing correction. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding etiology and developing new treatment approaches.

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Fig. 1: Manhattan plot of the observed –log10 P values of each variant for an association with depression in the meta-analysis (n =807,553; 246,363 cases and 561,190 controls).
Fig. 2: Significance of enrichment using a partitioned heritability approach.
Fig. 3: Chord diagram of genes significantly associated (P < 2.80 × 10−6) with depression and the second-level Anatomical Therapeutic Chemical classifications to which interacting drugs have been assigned.

Code availability

All code is available upon reasonable request from the corresponding author.

Data availability

Summary statistics for 10,000 genetic variants from the meta-analysis of 23andMe_307k, UK Biobank and PGC_139k and the summary statistics for all assessed genetic variants for the meta-analysis of depression in UK Biobank and PGC_139k are available from: https://doi.org/10.7488/ds/2458. To access the summary statistics for all assessed genetic variants for the meta-analysis of depression in 23andMe_307k, UK Biobank and PGC_139k, a data transfer agreement is required from 23andMe (dataset-request@23andMe.com) before a request is made to the corresponding author (D.Howard@ed.ac.uk). The raw genetic and phenotypic UK Biobank data used in this study, which were used under license, are available from: http://www.ukbiobank.ac.uk/. The genome-wide summary statistics for the Hyde et al. analysis of 23andMe, Inc. data were obtained under a data transfer agreement. Further information about obtaining access to the 23and Me, Inc. summary statistics are available from: https://research.23andme.com/collaborate/. The genome-wide summary statistics for the Wray et al. analysis of PGC data were obtained under secondary analysis proposals #60 and #76. Further information about obtaining access to the PGC summary statistics are available from: http://www.med.unc.edu/pgc/statgen.

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Acknowledgements

This research was conducted using the UK Biobank resource, application number 4844. We are grateful to the UK Biobank and all its voluntary participants. The UK Biobank study was conducted under generic approval from the NHS National Research Ethics Service (approval letter dated June 17, 2011, Ref 11/NW/0382). All participants gave full informed written consent. The authors acknowledge the help, advice and support from all members of the UK Biobank Psychiatric Genetics Group. The BiDirect cohort and the Münster cohort were approved by the ethics committee of the University of Münster and the Westphalian Chamber of Physicians in Münster, North-Rhine-Westphalia, Germany, and written informed consent was obtained from all participants. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” (STRADL) Reference 104036/Z/14/Z). Ethics approval for the Generation Scotland was given by the NHS Tayside committee on research ethics (reference 15/ES/0040), and all participants provided written informed consent for the use of their data. The study protocol used by 23andMe was approved by an external AAHRPP-accredited institutional review board. Details of the manuscripts containing approvals for the 35 PGC cohorts are listed in Supplementary Table 1 and Supplementary Table 2 of Wray et al9. We are grateful to the participants and research teams behind the Psychiatric Genomics Consortium, UK Biobank and 23andMe. We thank the following members of the 23andMe Research Team: M. Agee, B. Alipanahi, A. Auton, R. K. Bell, K. Bryc, S. L. Elson, P. Fontanillas, N. A. Furlotte, B. Hicks, K. E. Huber, E. M. Jewett, Y. Jiang, A. Kleinman, K.-Han. Lin, N. K. Litterman, M. H. McIntyre, J. L. Mountain, E. S. Noblin, C. A.M. Northover, S. J. Pitts, G. D. Poznik, J. F. Sathirapongsasuti, O. V. Sazonova, J. F. Shelton, S. Shringarpure, J. Y. Tung, V. Vacic, X. Wang and C. H. Wilson. We would like to thank N. Skene for his advice on analyzing the expression-weighted enrichment for brain cell types and N. Martin for his suggestions on polygenic risk scores. A.M.McI and I.J.D. acknowledge support from the Wellcome Trust (Wellcome Trust Strategic Award ‘STratifying Resilience and Depression Longitudinally’ (STRADL) Reference 104036/Z/14/Z and the Dr Mortimer and Theresa Sackler Foundation. I.J.D. is supported by the Centre for Cognitive Ageing and Cognitive Epidemiology, which is funded by the Medical Research Council and the Biotechnology and Biological Sciences Research Council (MR/K026992/1). This investigation represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. G.H. is supported by the Wellcome Trust (208806/Z/17/Z). D.J.S. is supported by the Lister Institute Prize Fellowship 2016-2021. N.R.W. acknowledges NMHRC grants 1078901 and 1087889. The BiDirect Study is supported by grants of the German Ministry of Research and Education (BMBF) to the University of Münster (01ER0816 and 01ER1506). The Münster cohort was funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5-1 and DA1151/5-2 to U.D.; SFB-TRR58, Projects C09 and Z02 to U.D.) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to U.D.). The PGC has received major funding from the US National Institute of Mental Health and the US National Institute of Drug Abuse (U01 MH109528 and U01 MH1095320).

Author information

Author notes

  1. A list of members and affiliations appears in the Supplementary Note.

Authors and Affiliations

  1. Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK

    David M. Howard, Mark J. Adams, Toni-Kim Clarke, Jonathan D. Hafferty, Jude Gibson, Masoud Shirali, Eleanor M. Wigmore, Clara Alloza, Xueyi Shen, Miruna C. Barbu, Eileen Y. Xu, Heather C. Whalley & Andrew M. McIntosh

  2. Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Jonathan R. I. Coleman, Saskia P. Hagenaars, Gerome Breen & Cathryn M. Lewis

  3. NIHR Biomedical Research Centre for Mental Health, South London and Maudsley NHS Trust, London, UK

    Jonathan R. I. Coleman, Saskia P. Hagenaars, Gerome Breen & Cathryn M. Lewis

  4. Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK

    Joey Ward & Daniel J. Smith

  5. Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK

    Riccardo E. Marioni, David J. Porteous, Gail Davies & Ian J. Deary

  6. Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK

    Riccardo E. Marioni & David J. Porteous

  7. Department of Psychology, University of Edinburgh, Edinburgh, UK

    Gail Davies, Ian J. Deary & Andrew M. McIntosh

  8. Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health, Sciences, Bristol Medical School, University of Bristol, Bristol, UK

    Gibran Hemani

  9. Institute of Epidemiology & Social Medicine, University of Münster, Münster, Germany

    Klaus Berger, Henning Teismann & Rajesh Rawal

  10. Department of Psychiatry, University of Münster, Münster, Germany

    Volker Arolt & Udo Dannlowski

  11. Department of Psychiatry, University of Melbourne, Victoria, Australia

    Bernhard T. Baune

  12. Department of Psychiatry and Psychotherapy, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Katharina Domschke

  13. 23andMe, Inc, Mountain View, CA, USA

    Chao Tian & David A. Hinds

  14. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia

    Maciej Trzaskowski, Enda M. Byrne & Naomi R. Wray

  15. Department of Psychiatry, Charite Universitatsmedizin Berlin Campus Benjamin Franklin, Berlin, Germany

    Stephan Ripke

  16. Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

    Stephan Ripke

  17. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Stephan Ripke

  18. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Patrick F. Sullivan

  19. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    Patrick F. Sullivan

  20. Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA

    Patrick F. Sullivan

Authors
  1. David M. Howard
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  2. Mark J. Adams
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  3. Toni-Kim Clarke
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  4. Jonathan D. Hafferty
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  5. Jude Gibson
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  6. Masoud Shirali
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  7. Jonathan R. I. Coleman
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  8. Saskia P. Hagenaars
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  9. Joey Ward
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  12. Xueyi Shen
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  20. Gibran Hemani
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  21. Klaus Berger
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  34. Patrick F. Sullivan
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Consortia

23andMe Research Team

Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

Contributions

D.M.H., D.J.S., G.B., C.M.L., and A.M.McI. conceived the research project. M.J.A., J.R.I.C., J.W., D.J.S., G.B., and A.M.McI. determined the variables that formed the depression phenotypes within UK Biobank. D.M.H., M.J.A., J.R.I.C., R.E.M. and G.D. applied quality control to the UK Biobank data. M.J.A. ran the association analysis in UK Biobank. 23andMe Research Team provided the summary statistics from the Hyde, et al.8 analysis. Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, M.T., S.R., P.F.S., N.R.W. and C.M.L. provided the summary statistics from the Wray, et al.9 analysis with additional removal of overlapping cohorts. D.M.H. ran the meta-analysis of the three contributing cohorts. C.T. and D.A.H conducted the replication analysis in 23andMe for the significant variants in the meta-analysis. T.-K.C., S.P.H and E.Y.X. conducted the polygenic risk score analysis. K.B., H.T. and R.R. ascertained the BiDirect cohort, and the Münster cohort was ascertained by V.A., B.T.B., U.D. and K.D. T.-K.C. and D.M.H calculated the genetic correlations and partitioned the heritability component of depression. D.M.H., and G.H. performed the Mendelian randomization analyses. D.M.H. ran the MAGMA-based analyses with J.W., M.S., E.M.W., C.A., J.G., X.S. and M.C.B. examining the genes and gene sets identified. J.D.H., D.M.H. and A.M.McI. conducted the gene–drug interaction analysis. I.J.D., D.J.P., H.C.W., C.S.H., S.R., D.J.S., P.F.S., N.R.W., E.M.B., G.B., C.M.L. and A.M.McI. provided expertise of association study methodology and statistical analysis. D.M.H. oversaw the research project and serves as the primary contact for all communication. All authors commented on the manuscript.

Corresponding author

Correspondence to David M. Howard.

Ethics declarations

Competing interests

I.J.D. is a participant in UK Biobank. C.T., D.A.H., and Members of the 23andMe Research Team are employees of 23andMe, Inc. The authors report no other conflicts of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Quantile–quantile plot of the observed P values on those expected for each genetic variants association with depression in our meta-analysis (n = 807,553 individuals).

P-values were obtained from an inverse-variance weighted meta-analysis of summary statistics data from UK Biobank, Psychiatric Genomics Consortium and 23andMe. UK Biobank summary statistics were obtained using a linear association analysis with the results transformed to the logistic scale. Psychiatric Genomics Consortium and 23andMe data were analysed using an additive logistic model. All association analyses were two-sided tests.

Supplementary Figure 2

Odds ratios and 95% confidence intervals for major depressive disorder (MDD) in Generation Scotland (n = 6,946) based on polygenic risk score (PRS) deciles calculated from the current meta-analysis of depression (n = 765,884 individuals) and from summary statistics from the genome-wide association study of major depression conducted by Wray, et al.9 (n = 453,779 individuals).

Supplementary Figure 3 Significant genetic correlations (rG; P < 0.01, after false discovery rate correction for multiple testing) between depression (n = 807,553 individuals) and other behavioral and disease related traits using LD score regression.

A negative rG indicates that an earlier or lower value of a continuous trait (that is earlier father’s age of death or lower subjective well-being was associated with depression. A positive rG indicates that a later or higher value of a continuous trait (that is higher triglyceride level) was associated with depression. P-values were calculated by testing whether rG was significantly different from 0 using a two-sided test. The center values are the genetic correlation for each trait and the error bars are the respective standard error. LD score regression implemented in LD Hub software (http://ldsc.broadinstitute.org/).

Supplementary Figure 4 Mendelian randomization test for a putative causal effect of depression (n = 629,934 individuals; 56 instrumental variables) on neuroticism (n = 29,496 twin pairs) using inverse weighted regression, MR Egger and a weighted median test.

Center values reflect the effect of each instrumental variable on each trait with the respective error bars reflecting the standard error of the variable’s effect on each trait.

Supplementary Figure 5 Mendelian randomization analysis for a putative causal effect of depression (n = 629,934 individuals; 49 instrumental variables) on ever vs. never smoked (n = 69,409 individuals) using inverse weighted regression.

Center values reflect the effect of each instrumental variable on each trait with the respective error bars reflecting the standard error of the variable’s effect on each trait.

Supplementary Figure 6 Mendelian randomization test for a putative causal effect of neuroticism (n = 329,821 individuals; 65 instrumental variables) on depression (n = 537,701 individuals) using inverse weighted regression, MR Egger and a weighted median test.

Center values reflect the effect of each instrumental variable on each trait with the respective error bars reflecting the standard error of the variable’s effect on each trait.

Supplementary Figure 7 Contribution of functional annotation of 24 categories to the heritability of depression (n = 807,553 individuals) based on the variants within each category.

The enrichment of each functional category, shown on the y-axis, is calculated as the proportion of heritability assigned to a functional categories divided by the proportion of variants in that category (Pr(h2)/Pr(SNPs)). Error bars represent jackknife standard errors for each the estimate of enrichment, and an asterisk indicates significant enrichment after Bonferroni correction for multiple testing (Conserved P-value = 2.55 × 10−17; H3K4me1 P-value = 0.0015; Intron P-value = 0.0014). P-values were calculated using a one-sided test and tested whether there the estimate of enrichment was significantly different from zero enrichment. The dashed line represents the threshold for no enrichment.

Supplementary Figure 8 Enrichment estimate (β) of significantly enriched brain cell regions using GTEx overlaid on physical representation of brain anatomy (n = 807,553 individuals).

The pseudo-coloring highlights the coefficients of the brain regions in red that were significantly enriched (P < 0.05) for depression variants compared to no enrichment using a one-sided test and after Bonferroni correction for multiple testing.

Supplementary Figure 9 Stratified LD score regression analyses showing significance of enrichment estimates for 3 brain cell types in depression (n = 807,553 individuals).

The dashed line represents the Bonferroni threshold for significance for multiple testing correction (P = 0.0167) and * indicates significant enrichment for that brain cell type (Neuron P-value = 4.52 × 10−4). P-values were calculated based on evidence of enrichment compared to no enrichment using a one-sided test.

Supplementary Figure 10 Regional visualization plot centered on the independently-associated variant, rs1021363, close to the Sortilin-related VPS10 domain containing receptor 3 (SORCS3) gene on chromosome 10 (n = 807,553 individuals).

P-values were obtained from an inverse-variance weighted meta-analysis of summary statistics data from UK Biobank, Psychiatric Genomics Consortium and 23andMe. UK Biobank summary statistics were obtained using a linear association analysis with the results transformed to the logistic scale. Psychiatric Genomics Consortium and 23andMe data were analysed using an additive logistic model. All association analyses were two-sided tests. Recombination rates used in the plots are based on the European 1000 Genomes panel from November 2014.

Supplementary Figure 11 Regional visualization plots centered on independently-associated variants (A. rs2568958 and B. rs10890020) close to the neuronal growth regulator 1 (NEGR1) gene on chromosome 1 (n = 807,553 individuals).

P-values were obtained from an inverse-variance weighted meta-analysis of summary statistics data from UK Biobank, Psychiatric Genomics Consortium and 23andMe. UK Biobank summary statistics were obtained using a linear association analysis with the results transformed to the logistic scale. Psychiatric Genomics Consortium and 23andMe data were analysed using an additive logistic model. All association analyses were two-sided tests. Recombination rates used in the plots are based on the European 1000 Genomes panel from November 2014.

Supplementary Figure 12 Regional visualization plots centred on independently-associated variants (A. rs62091461, B. rs12966052, and C. rs12967143) close to the transcription factor 4 (TCF4) and RAB27B genes on chromosome 18 (n = 807,553 individuals).

P-values were obtained from an inverse-variance weighted meta-analysis of summary statistics data from UK Biobank, Psychiatric Genomics Consortium and 23andMe. UK Biobank summary statistics were obtained using a linear association analysis with the results transformed to the logistic scale. Psychiatric Genomics Consortium and 23andMe data were analysed using an additive logistic model. All association analyses were two-sided tests. Recombination rates used in the plots are based on the European 1000 Genomes panel from November 2014.

Supplementary information

Supplementary Figures 1–12

Supplementary Note

Reporting Summary

Supplementary Table 1

Variants with a P-value < 5 × 10-8 for an association with depression (n = 807,553 individuals) in the meta-analysis of PGC_139k, 23andMe_307k and UK Biobank. Effects sizes and allele frequencies are reported for the A1 allele.

Supplementary Table 2

The direction of effect of previously reported significant variants for depression across the studies contributing to the meta-analysis.

Supplementary Table 3

Genetic correlations between depression (n = 807,553 individuals) and other behavioural and disease related traits using LD score regression implemented in LD Hub software (http://ldsc.broadinstitute.org/).

Supplementary Table 4

Mendelian randomization analysis between depression and other traits using MR Egger test for directional horizontal pleiotropy, inverse variance weighted (IVW) test for variant heterogeneity and IVW regression, weighted median and MR-Egger tests for a causal effect.

Supplementary Table 5

Heritability partitioned by functional annotation enrichment (n = 807,553 individuals). The asterisk indicates significance after Bonferroni correction for multiple testing (P < 0.0021).

Supplementary Table 6

Partitioning of the heritability estimate by cell type enrichment (n = 807,553 individuals). The asterisk indicates significance after Bonferroni correction for multiple testing (P < 0.0056).

Supplementary Table 7

Enrichment estimates for brain regions using GTEx (n = 807,553 individuals). The asterisk indicates significance after Bonferroni correction for multiple testing (P < 0.0038).

Supplementary Table 8

Enrichment estimates for brain cell types (n = 807,553 individuals). The asterisk indicates significance after Bonferroni correction for multiple testing (P < 0.0167).

Supplementary Table 9

Genome-wide significant gene-based hits (P < 2.80 x 10-6) in the meta-analysis of depression using MAGMA. NSNPS is the number of SNPs in the gene; NiSNPs is the number of independent SNPs in the gene.

Supplementary Table 10

Number and proportion of gene overlap within the Gene Ontology Consortium gene-sets associated (Pcorrected < 0.05) with depression.

Supplementary Table 11

Drug x gene interactions for the genes identified as significantly associated with depression with interactions obtained from the drug gene interaction database (http://dgidb.genome.wustl.edu/). The Anatomical Therapeutic Chemical (ATC) classification for each drug is provided along with the type of interaction and its source.

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Howard, D.M., Adams, M.J., Clarke, TK. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci 22, 343–352 (2019). https://doi.org/10.1038/s41593-018-0326-7

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